The classic augmented Lagrangian Alternating Direction Method (ADM) has recently found great utilities in solving convex, separable optimization problems arising from signal/image processing and sparse optimization. In this talk, we first give some recent examples of ADM applications, including extensions to solving non-convex and non-separable problems. We then present new convergence results that extend the classic ADM convergence theory in several aspects.
Copyright. All Rights Reserved.